[ADMB Users] glmmADMB in R:question regarding 'residuals(Model)'

[Schweizer, Peter E.]

Dear colleagues,

We are using glmmADMB in R to model land cover and water quality influence on species diversity of fishes within a study area with several subregions.
We defined subregion as a random factor and also ask for individual intercepts for the different subregions.
A 'global' model for overdispersed count data was formulated as
GM<-glmm.admb( N_Species ~ b1 + b2 + b3 + ...+ bn + Subregion, random = ~ 1, group="Subregion", data=input, family="nbinom")
We subsequently evaluated several candidate models that represent various subsets of variables from the global model.
Our input file is A1, with A1$NO representing the observed number of species. During the process of examining model performance we used
Observed - Predicted   (A1$NO -(fitted(best)))  for the 'best' model based on lowest AICc to derive residuals for predicted Nspecies. However, using 'residuals(best)' produced considerable different (smaller) values which we find somehow puzzling. Are we wrong to assume that (Nspecies predicted by 'best' model,  + residuals(best)) should add up to Nspecies observed (A1$NO)?

Any insight would be appreciated.

Peter
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